def peaks_to_niftis(pam,
fname_shm,
fname_dirs,
fname_values,
fname_indices,
fname_gfa,
reshape_dirs=False):
""" Save SH, directions, indices and values of peaks to Nifti.
"""
save_nifti(fname_shm, pam.shm_coeff.astype(np.float32), pam.affine)
if reshape_dirs:
pam_dirs = reshape_peaks_for_visualization(pam)
else:
pam_dirs = pam.peak_dirs.astype(np.float32)
save_nifti(fname_dirs, pam_dirs, pam.affine)
save_nifti(fname_values, pam.peak_values.astype(np.float32),
pam.affine)
save_nifti(fname_indices, pam.peak_indices, pam.affine)
save_nifti(fname_gfa, pam.gfa, pam.affine)
def test_reshape_peaks_for_visualization():
data1 = np.random.randn(10, 5, 3).astype('float32')
data2 = np.random.randn(10, 2, 5, 3).astype('float32')
data3 = np.random.randn(10, 2, 12, 5, 3).astype('float32')
data1_reshape = reshape_peaks_for_visualization(data1)
data2_reshape = reshape_peaks_for_visualization(data2)
data3_reshape = reshape_peaks_for_visualization(data3)
assert_array_equal(data1_reshape.shape, (10, 15))
assert_array_equal(data2_reshape.shape, (10, 2, 15))
assert_array_equal(data3_reshape.shape, (10, 2, 12, 15))
assert_array_equal(data1_reshape.reshape(10, 5, 3), data1)
assert_array_equal(data2_reshape.reshape(10, 2, 5, 3), data2)
assert_array_equal(data3_reshape.reshape(10, 2, 12, 5, 3), data3)
def test_reshape_peaks_for_visualization():
data1 = np.random.randn(10, 5, 3).astype('float32')
data2 = np.random.randn(10, 2, 5, 3).astype('float32')
data3 = np.random.randn(10, 2, 12, 5, 3).astype('float32')
data1_reshape = reshape_peaks_for_visualization(data1)
data2_reshape = reshape_peaks_for_visualization(data2)
data3_reshape = reshape_peaks_for_visualization(data3)
assert_array_equal(data1_reshape.shape, (10, 15))
assert_array_equal(data2_reshape.shape, (10, 2, 15))
assert_array_equal(data3_reshape.shape, (10, 2, 12, 15))
assert_array_equal(data1_reshape.reshape(10, 5, 3), data1)
assert_array_equal(data2_reshape.reshape(10, 2, 5, 3), data2)
assert_array_equal(data3_reshape.reshape(10, 2, 12, 5, 3), data3)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.afd = args.afd or 'afd_max.nii.gz'
args.afd_total = args.afd_total or 'afd_total_sh0.nii.gz'
args.afd_sum = args.afd_sum or 'afd_sum.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
arglist = [args.afd, args.afd_total, args.afd_sum, args.nufo, args.peaks]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least '
'one file to output.')
assert_inputs_exist(parser, [])
assert_outputs_exists(parser, args, arglist)
data, affine = load(args.input)
if args.mask is None:
mask = np.ones(data.shape[:-1])
else:
mask, affine2 = load(args.mask)
nufo_map, afd_map, afd_sum, peaks_dirs = get_maps(data, mask, args)
# Save result
if args.nufo:
save(nufo_map, affine, args.nufo)
if args.afd:
save(afd_map, affine, args.afd)
if args.afd_total:
# this is the analytical afd total
afd_tot = data[:, :, :, 0]
save(afd_tot, affine, args.afd_total)
if args.afd_sum:
save(afd_sum, affine, args.afd_sum)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks_dirs),
affine), args.peaks)
if args.visu:
if nufo_map.max() > nufo_map.min():
nufo_map = (255 * (nufo_map - nufo_map.min()) /
(nufo_map.max() - nufo_map.min()))
if afd_map.max() > afd_map.min():
afd_map = (255 * (afd_map - afd_map.min()) /
(afd_map.max() - afd_map.min()))
save(nufo_map, affine, args.nufo, True)
save(afd_map, affine, args.afd, True)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
# Checking args
if not args.not_all:
args.fodf = args.fodf or 'fodf.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peak_indices.nii.gz'
arglist = [args.fodf, args.peaks, args.peak_indices]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least '
'one file to output.')
assert_inputs_exist(parser,
[args.input, args.bvals, args.bvecs, args.frf_file])
assert_outputs_exist(parser, args, arglist)
full_frf = np.loadtxt(args.frf_file)
vol = nib.load(args.input)
data = vol.dataobj
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if args.mask is None:
mask = None
else:
mask = np.asanyarray(nib.load(args.mask).dataobj).astype(np.bool)
# Computing fODF
peaks_csd = compute_fodf(data,
bvals,
bvecs,
full_frf,
sh_order=args.sh_order,
nbr_processes=args.nbr_processes,
mask=mask,
sh_basis=args.sh_basis,
return_sh=True,
force_b0_threshold=args.force_b0_threshold)
# Saving results
if args.fodf:
nib.save(
nib.Nifti1Image(peaks_csd.shm_coeff.astype(np.float32),
vol.affine), args.fodf)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks_csd),
vol.affine), args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(peaks_csd.peak_indices, vol.affine),
args.peak_indices)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
if not args.not_all:
args.fodf = args.fodf or 'fodf.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peak_indices.nii.gz'
arglist = [args.fodf, args.peaks, args.peak_indices]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least '
'one file to output.')
assert_inputs_exist(parser,
[args.input, args.bvals, args.bvecs, args.frf_file])
assert_outputs_exist(parser, args, arglist)
nbr_processes = args.nbr_processes
parallel = True
if nbr_processes is not None:
if nbr_processes <= 0:
nbr_processes = None
elif nbr_processes == 1:
parallel = False
full_frf = np.loadtxt(args.frf_file)
if not full_frf.shape[0] == 4:
raise ValueError('FRF file did not contain 4 elements. '
'Invalid or deprecated FRF format')
frf = full_frf[0:3]
mean_b0_val = full_frf[3]
vol = nib.load(args.input)
data = vol.get_data()
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Raise warning for sh order if there is not enough DWIs
if data.shape[-1] < (args.sh_order + 1) * (args.sh_order + 2) / 2:
warnings.warn(
'We recommend having at least {} unique DWIs volumes, but you '
'currently have {} volumes. Try lowering the parameter --sh_order '
'in case of non convergence.'.format(
(args.sh_order + 1) * (args.sh_order + 2) / 2, data.shape[-1]))
reg_sphere = get_sphere('symmetric362')
peaks_sphere = get_sphere('symmetric724')
csd_model = ConstrainedSphericalDeconvModel(gtab, (frf, mean_b0_val),
reg_sphere=reg_sphere,
sh_order=args.sh_order)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_sh=True,
sh_basis_type=args.sh_basis,
sh_order=args.sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
if args.fodf:
nib.save(
nib.Nifti1Image(peaks_csd.shm_coeff.astype(np.float32),
vol.affine), args.fodf)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks_csd),
vol.affine), args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(peaks_csd.peak_indices, vol.affine),
args.peak_indices)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.afd_max = args.afd_max or 'afd_max.nii.gz'
args.afd_total = args.afd_total or 'afd_total_sh0.nii.gz'
args.afd_sum = args.afd_sum or 'afd_sum.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_values = args.peak_values or 'peak_values.nii.gz'
args.peak_indices = args.peak_indices or 'peak_indices.nii.gz'
arglist = [args.afd_max, args.afd_total, args.afd_sum, args.nufo,
args.rgb, args.peaks, args.peak_values,
args.peak_indices]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least '
'one file to output.')
assert_inputs_exist(parser, args.in_fODF)
assert_outputs_exist(parser, args, arglist)
vol = nib.load(args.in_fODF)
data = vol.get_fdata(dtype=np.float32)
affine = vol.affine
if args.mask is None:
mask = None
else:
mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
if mask.shape != data.shape[:-1]:
raise ValueError("Mask is not the same shape as data.")
sphere = get_sphere(args.sphere)
# Computing peaks
peak_dirs, peak_values, \
peak_indices = peaks_from_sh(data,
sphere,
mask=mask,
relative_peak_threshold=args.r_threshold,
absolute_threshold=args.a_threshold,
min_separation_angle=25,
normalize_peaks=False,
sh_basis_type=args.sh_basis,
nbr_processes=args.nbr_processes)
# Computing maps
nufo_map, afd_max, afd_sum, rgb_map, \
_, _ = maps_from_sh(data, peak_dirs,
peak_values, peak_indices,
sphere, nbr_processes=args.nbr_processes)
# Save result
if args.nufo:
nib.save(nib.Nifti1Image(nufo_map.astype(np.float32),
affine), args.nufo)
if args.afd_max:
nib.save(nib.Nifti1Image(afd_max.astype(np.float32),
affine), args.afd_max)
if args.afd_total:
# this is the analytical afd total
afd_tot = data[:, :, :, 0]
nib.save(nib.Nifti1Image(afd_tot.astype(np.float32),
affine), args.afd_total)
if args.afd_sum:
nib.save(nib.Nifti1Image(afd_sum.astype(np.float32),
affine), args.afd_sum)
if args.rgb:
nib.save(nib.Nifti1Image(rgb_map.astype('uint8'), affine), args.rgb)
if args.peaks or args.peak_values:
peak_values = np.divide(peak_values, peak_values[..., 0, None],
out=np.zeros_like(peak_values),
where=peak_values[..., 0, None]!=0)
peak_dirs[...] *= peak_values[..., :, None]
if args.peaks:
nib.save(nib.Nifti1Image(reshape_peaks_for_visualization(peak_dirs),
affine), args.peaks)
if args.peak_values:
nib.save(nib.Nifti1Image(peak_values, vol.affine), args.peak_values)
if args.peak_indices:
nib.save(nib.Nifti1Image(peak_indices, vol.affine), args.peak_indices)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.gfa = args.gfa or 'gfa.nii.gz'
args.peaks = args.peaks or 'peaks.nii.gz'
args.peak_indices = args.peak_indices or 'peaks_indices.nii.gz'
args.sh = args.sh or 'sh.nii.gz'
args.nufo = args.nufo or 'nufo.nii.gz'
args.a_power = args.a_power or 'anisotropic_power.nii.gz'
arglist = [
args.gfa, args.peaks, args.peak_indices, args.sh, args.nufo,
args.a_power
]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least ' +
'one file to output.')
assert_inputs_exist(parser, [args.in_dwi, args.in_bval, args.in_bvec])
assert_outputs_exist(parser, args, arglist)
validate_nbr_processes(parser, args)
nbr_processes = args.nbr_processes
parallel = nbr_processes > 1
# Load data
img = nib.load(args.in_dwi)
data = img.get_fdata(dtype=np.float32)
bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
sphere = get_sphere('symmetric724')
mask = None
if args.mask:
mask = get_data_as_mask(nib.load(args.mask))
# Sanity check on shape of mask
if mask.shape != data.shape[:-1]:
raise ValueError('Mask shape does not match data shape.')
if args.use_qball:
model = QballModel(gtab, sh_order=args.sh_order, smooth=DEFAULT_SMOOTH)
else:
model = CsaOdfModel(gtab,
sh_order=args.sh_order,
smooth=DEFAULT_SMOOTH)
odfpeaks = peaks_from_model(model=model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True,
return_sh=True,
sh_order=int(args.sh_order),
sh_basis_type=args.sh_basis,
npeaks=5,
parallel=parallel,
nbr_processes=nbr_processes)
if args.gfa:
nib.save(nib.Nifti1Image(odfpeaks.gfa.astype(np.float32), img.affine),
args.gfa)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(odfpeaks),
img.affine), args.peaks)
if args.peak_indices:
nib.save(nib.Nifti1Image(odfpeaks.peak_indices, img.affine),
args.peak_indices)
if args.sh:
nib.save(
nib.Nifti1Image(odfpeaks.shm_coeff.astype(np.float32), img.affine),
args.sh)
if args.nufo:
peaks_count = (odfpeaks.peak_indices > -1).sum(3)
nib.save(nib.Nifti1Image(peaks_count.astype(np.int32), img.affine),
args.nufo)
if args.a_power:
odf_a_power = anisotropic_power(odfpeaks.shm_coeff)
nib.save(nib.Nifti1Image(odf_a_power.astype(np.float32), img.affine),
args.a_power)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not args.not_all:
args.cos_asym_map = args.cos_asym_map or 'cos_asym_map.nii.gz'
args.odd_power_map = args.odd_power_map or 'odd_power_map.nii.gz'
args.peaks = args.peaks or 'asym_peaks.nii.gz'
args.peak_values = args.peak_values or 'asym_peak_values.nii.gz'
args.peak_indices = args.peak_indices or 'asym_peak_indices.nii.gz'
args.nupeaks = args.nupeaks or 'nupeaks.nii.gz'
arglist = [
args.cos_asym_map, args.odd_power_map, args.peaks, args.peak_values,
args.peak_indices, args.nupeaks
]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least '
'one file to output.')
inputs = [args.in_sh]
if args.mask:
inputs.append(args.mask)
assert_inputs_exist(parser, inputs)
assert_outputs_exist(parser, args, arglist)
sh_img = nib.load(args.in_sh)
sh = sh_img.get_fdata()
sphere = get_sphere(args.sphere)
sh_order, full_basis = get_sh_order_and_fullness(sh.shape[-1])
if not full_basis:
parser.error('Invalid SH image. A full SH basis is expected.')
if args.mask:
mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
else:
mask = np.sum(np.abs(sh), axis=-1) > 0
if args.cos_asym_map:
cos_asym_map = compute_cos_asym_map(sh, sh_order, mask)
nib.save(nib.Nifti1Image(cos_asym_map, sh_img.affine),
args.cos_asym_map)
if args.odd_power_map:
odd_power_map = compute_odd_power_map(sh, sh_order, mask)
nib.save(nib.Nifti1Image(odd_power_map, sh_img.affine),
args.odd_power_map)
if args.peaks or args.peak_values or args.peak_indices or args.nupeaks:
peaks, values, indices =\
peaks_from_sh(sh, sphere, mask=mask,
relative_peak_threshold=args.r_threshold,
absolute_threshold=args.a_threshold,
min_separation_angle=25,
normalize_peaks=False,
# because v and -v are unique, we want twice
# the usual default value (5) of npeaks
npeaks=10,
sh_basis_type=args.sh_basis,
nbr_processes=args.nbr_processes,
full_basis=True,
is_symmetric=False)
if args.peaks:
nib.save(
nib.Nifti1Image(reshape_peaks_for_visualization(peaks),
sh_img.affine), args.peaks)
if args.peak_values:
nib.save(nib.Nifti1Image(values, sh_img.affine), args.peak_values)
if args.peak_indices:
nib.save(nib.Nifti1Image(indices.astype(np.uint8), sh_img.affine),
args.peak_indices)
if args.nupeaks:
nupeaks = np.count_nonzero(values, axis=-1).astype(np.uint8)
nib.save(nib.Nifti1Image(nupeaks, sh_img.affine), args.nupeaks)
